Settings We use the MXNet package for implementing our proposed approach. All the experiments were conducted using one NVIDIA GeForce RTX 2080 Ti, 11 GB memory. For pre-processing input images, we resize them to \(256\times 256\) , randomly crop them to size \(227\times 227\) , horizontally flip them left or right. We use an ImageNet ILSVRC [1]} pre-trained GoogLeNet [2]} with a fully connected randomly initialized layer as our feature extracting network. For a fair comparison, we compare with methods that use the same network. Results and comparisons on other network architectures have been included in the supplementary. Throughout the experiments, we use 512-dimensional feature embedding vectors. We use the Adam optimizer [3]} for training our models, with \(\beta _1=0.9, \beta _2=0.999\) , learning rate of \(10^{-4}\) . The hyperparameters \(\alpha _m\)  and \(\beta _m\)  are set as 2 and 50, respectively. For Cars196, CUB-200-2011 datasets, we use a batch size of 32, and for the SOP dataset, we set the batch size to 128. The SOTA methods use a larger batch size of 120 or 128 for the Cars196, CUB-200-2011 dataset as well. As shown in Section REF , larger batch size provides a boost in performance metrics. t-SNE visualizations at different epochs demonstrating the training process can be found in the supplementary.
